maxim.integrand {RiskMap} | R Documentation |
Maximizes the integrand function for Generalized Linear Gaussian Process Models (GLGPMs), which involves the evaluation of likelihood functions with spatially correlated random effects.
maxim.integrand(
y,
units_m,
mu,
Sigma,
ID_coords,
ID_re = NULL,
family,
sigma2_re = NULL,
hessian = FALSE,
gradient = FALSE
)
y |
Response variable vector. |
units_m |
Units of measurement for the response variable. |
mu |
Mean vector of the response variable. |
Sigma |
Covariance matrix of the spatial process. |
ID_coords |
Indices mapping response to locations. |
ID_re |
Indices mapping response to unstructured random effects. |
family |
Distribution family for the response variable. Must be one of 'gaussian', 'binomial', or 'poisson'. |
sigma2_re |
Variance of the unstructured random effects. |
hessian |
Logical; if TRUE, compute the Hessian matrix. |
gradient |
Logical; if TRUE, compute the gradient vector. |
This function maximizes the integrand for GLGPMs using the Nelder-Mead optimization algorithm. It computes the likelihood function incorporating spatial covariance and unstructured random effects, if provided.
The integrand includes terms for the spatial process (Sigma), unstructured random effects (sigma2_re), and the likelihood function (llik) based on the specified distribution family ('gaussian', 'binomial', or 'poisson').
A list containing the mode estimate, and optionally, the Hessian matrix and gradient vector.
Emanuele Giorgi e.giorgi@lancaster.ac.uk
Claudio Fronterre c.fronterr@lancaster.ac.uk